Motion segmentation
shares some similarity with unsupervised VOS,
but focuses on discovering moving objects.
In the literature,
[1]}, [2]}, [3]}, [4]}, [5]} consider clustering the pixels with similar motion patterns;
[6]}, [7]}, [8]} train deep networks to map the motions to segmentation masks.
Another line of work has tackled the problem by explicitly leveraging the independence, in the flow field, between the moving object and its background.
For instance, [9]} proposes an adversarial setting, where a generator is trained to produce masks, altering the input flow, such that the inpainter fails to estimate the missing information.
In [10]}, [11]}, [12]},
the authors propose to highlight the independently moving object by compensating for the background motion,
either by registering consecutive frames, or explicitly estimating camera motion.
In constrained scenarios, such as the autonomous driving domain,
[13]} proposes to jointly optimize depth, camera motion, optical flow and motion segmentation.
